Exploring daily wind data using the Meteostat Python Library
Group Members: Travis, Ira, Micah
Course: Data Science – Phase 1
Goal: Explore and compare wind trends in distinct U.S. regions
Source: Meteostat Python API
Dataset Type: Aggregated weather observations per station
Key Variableswspd: Average wind speed (km/h)wdir: Mean wind direction (degrees)tavg: Average air temperature (°C)coco: Condition codeTime Period: 2024
Locations: ~30
Frame: Hourly, Daily, Monthly with a focus on Hourly
Units: Metric (km/h, degrees, °C)
#Start of Data ::: {.panel-tabset}
| Regional Wind Analysis by Speed and Direction | ||||
| Hourly Averages 2024 | Data: Meteostat | ||||
| latitude | longitude | Wind Statistics | ||
|---|---|---|---|---|
| Speed (km/h) | Direction (°) | |||
| Case Studies | ||||
| Key West, FL | 24.5551 | -81.78 | 68.3 | 91.0 |
| Honolulu, HI | 21.3069 | -157.8583 | 64.1 | 54.0 |
| Oklahoma City, OK | 35.4676 | -97.5164 | 63.3 | 142.0 |
| Anchorage, AK | 61.2181 | -149.9003 | 29.2 | 358.0 |
| Midwest | ||||
| Cleveland, OH | 41.4993 | -81.6944 | 64.5 | 227.0 |
| Chicago, IL | 41.8781 | -87.6298 | 57.0 | 259.0 |
| Detroit, MI | 42.3314 | -83.0458 | 55.9 | 248.0 |
| Des Moines, IA | 41.5868 | -93.625 | 55.5 | 246.0 |
| Milwaukee, WI | 43.0389 | -87.9065 | 54.5 | 297.0 |
| Minneapolis, MN | 44.9778 | -93.265 | 45.4 | 307.0 |
| Kansas City, MO | 39.0997 | -94.5786 | 44.0 | 2.0 |
| Northeast | ||||
| Buffalo, NY | 42.8864 | -78.8784 | 74.1 | 243.0 |
| Boston, MA | 42.3601 | -71.0589 | 61.5 | 278.0 |
| Philadelphia, PA | 39.9526 | -75.1652 | 49.9 | 297.0 |
| Albany, NY | 42.6526 | -73.7562 | 43.3 | 263.0 |
| Portland, ME | 43.6591 | -70.2568 | 41.2 | 313.0 |
| Pittsburgh, PA | 40.4406 | -79.9959 | 40.7 | 299.0 |
| New York, NY | 40.7128 | -74.006 | 38.3 | 300.0 |
| Southeast | ||||
| New Orleans, LA | 29.9511 | -90.0715 | 69.6 | 133.0 |
| Jacksonville, FL | 30.3322 | -81.6557 | 52.3 | 81.0 |
| Miami, FL | 25.7617 | -80.1918 | 47.3 | 81.0 |
| Tampa, FL | 27.9506 | -82.4572 | 41.8 | 49.0 |
| Charlotte, NC | 35.2271 | -80.8431 | 36.5 | 319.0 |
| Raleigh, NC | 35.7796 | -78.6382 | 34.0 | 345.0 |
| Atlanta, GA | 33.749 | -84.388 | 28.6 | 357.0 |
| West | ||||
| Denver, CO | 39.7392 | -104.9903 | 48.6 | 180.0 |
| San Francisco, CA | 37.7749 | -122.4194 | 48.2 | 294.0 |
| Salt Lake City, UT | 40.7608 | -111.891 | 47.0 | 154.0 |
| Las Vegas, NV | 36.1699 | -115.1398 | 45.2 | 319.0 |
| Los Angeles, CA | 34.0522 | -118.2437 | 39.6 | 208.0 |
| Portland, OR | 45.5152 | -122.6784 | 39.2 | 333.0 |
| Phoenix, AZ | 33.4484 | -112.074 | 37.7 | 128.0 |
| Seattle, WA | 47.6062 | -122.3321 | 30.0 | 191.0 |
| Legend: 🔵North 🔴East 🟡South 🟢West | Darker = Stronger | ||||
| city_name | Albany, NY | Anchorage, AK | Atlanta, GA | Boston, MA | Buffalo, NY | Charlotte, NC | Chicago, IL | Cleveland, OH | Denver, CO | Des Moines, IA | ... | Philadelphia, PA | Phoenix, AZ | Pittsburgh, PA | Portland, ME | Portland, OR | Raleigh, NC | Salt Lake City, UT | San Francisco, CA | Seattle, WA | Tampa, FL |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| region | |||||||||||||||||||||
| Case Studies | NaN | 29.2 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| Midwest | NaN | NaN | NaN | NaN | NaN | NaN | 57.0 | 64.5 | NaN | 55.5 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| Northeast | 43.3 | NaN | NaN | 61.5 | 74.1 | NaN | NaN | NaN | NaN | NaN | ... | 49.9 | NaN | 40.7 | 41.2 | NaN | NaN | NaN | NaN | NaN | NaN |
| Southeast | NaN | NaN | 28.6 | NaN | NaN | 36.5 | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | 34.0 | NaN | NaN | NaN | 41.8 |
| West | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 48.6 | NaN | ... | NaN | 37.7 | NaN | NaN | 39.2 | NaN | 47.0 | 48.2 | 30.0 | NaN |
5 rows × 33 columns
How do wind patterns change by region?
What are some case studies of extreme weather?
How do geographical features (lakes, oceans, mountains, deserts, plains) impact wind patterns?